Future work will focus inclusion of a suitable phase factor within the MGF facilitating OA pulses building up at correct temporal locations for an acoustically inhomogeneous source.This paper proposes a robust system for detecting North Atlantic right whales by using deep learning methods to denoise noisy recordings. Passive acoustic recordings of right whale vocalisations are subject to noise contamination from many sources, such as shipping and offshore activities. When such data are applied to uncompensated classifiers, accuracy falls substantially. To build robustness into the detection process, two separate approaches that have proved successful for image denoising are considered. Specifically, a denoising convolutional neural network and a denoising autoencoder, each of which is applied to spectrogram representations of the noisy audio signal, are developed. Performance is improved further by matching the classifier training to include the vestigial signal that remains in clean estimates after the denoising process. Evaluations are performed first by adding white, tanker, trawler, and shot noises at signal-to-noise ratios from -10 to +5 dB to clean recordings to simulate noisy conditions. Experiments show that denoising gives substantial improvements to accuracy, particularly when using the vestigial-trained classifier. A final test applies the proposed methods to previously unseen noisy right whale recordings and finds that denoising is able to improve performance over the baseline clean-trained model in this new noise environment.Dynamic directivity is a specific characteristic of the human voice, showing time-dependent variations while speaking or singing. To study and model the human voice's articulation-dependencies and provide datasets that can be applied in virtual acoustic environments, full-spherical voice directivity measurements were carried out for 13 persons while articulating eight phonemes. Since it is nearly impossible for subjects to repeat exactly the same articulation numerous times, the sound radiation was captured simultaneously using a surrounding spherical microphone array with 32 microphones and then subsequently spatially upsampled to a dense sampling grid. Based on these dense directivity patterns, the spherical voice directivity was studied for different phonemes, and phoneme-dependent variations were analyzed. The differences between the phonemes can, to some extent, be explained by articulation-dependent properties, e.g., the mouth opening size. The directivity index, averaged across all subjects, varied by a maximum of 3?dB between any of the vowels or fricatives, and statistical analysis showed that these phoneme-dependent differences are significant.Many claims about the prevalence of phonetic voicing in English obstruents have been made in the literature over the decades, particularly concerning the stops and affricate [b, d, ?, ?]. An examination of this literature reveals that many of these claims are based on a paucity of speech data and measurements. For the present study, voiced consonants in the Buckeye corpus of American English (39 speakers) have been measured to determine the percentage of their duration that shows vocal cord vibrations. The prevalence of voicing in the 53?690 voiced stop and affricate tokens has been examined in all contexts, including the initial, intervocalic, and final positions. The results generally contradict the common notion that the nominally "voiced" stops of English are phonetically unvoiced in all positions but intervocalic. Here, they are found to be mostly voiced in final position as well as intervocalically, but usually less than 50% voiced in initial position. A significant proportion of these stops, however, were found to be nearly 100% voiced in the initial position, and this could not be explained by interspeaker variation.The Reflections series takes a look back on historical articles from The Journal of the Acoustical Society of America that have had a significant impact on the science and practice of acoustics.Broadband echosounders measure the scattering response of an organism over a range of frequencies. When compared with acoustic scattering models, this response can provide insight into the type of organism measured. Here, we train the k-Nearest Neighbors algorithm using scattering models and use it to group target spectra (25-40?kHz) measured in the mesopelagic near the New England continental shelf break. Compared to an unsupervised approach, this creates groupings defined by their scattering physics and does not require significant tuning. The model classifies human-annotated target spectra as gas-bearing organisms (at, below, or above resonance) or fluid-like organisms with a weighted F1-score of 0.90. Class-specific F1-scores varied-the F1-score exceeded 0.89 for all gas-bearing organisms, while fluid-like organisms were classified with an F1-score of 0.73. Analysis of classified target spectra provides insight into the size and distribution of organisms in the mesopelagic and allows for the assessment of assumptions used to calculate organism abundance. Organisms with resonance peaks between 25 and 40?kHz account for 43% of detections, but a disproportionately high fraction of volume backscatter. Results suggest gas bearing organisms account for 98.9% of volume backscattering concurrently measured using a 38?kHz shipboard echosounder between 200 and 800?m depth.The source level (SL) and vocalizing source depth (SD) of individuals from two blue whale (BW) subspecies, an Antarctic blue whale (Balaenoptera musculus intermedia; ABW) and a Madagascar pygmy blue whale (Balaenoptera musculus brevicauda; MPBW) are estimated from a single bottom-mounted hydrophone in the western Indian Ocean. Stereotyped units (male) are automatically detected and the range is estimated from the time delay between the direct and lowest-order multiply-reflected acoustic paths (multipath-ranging). Allowing for geometric spreading and the Lloyd's mirror effect (range-, depth-, and frequency-dependent) SL and SD are estimated by minimizing the SL variance over a series of units from the same individual over time (and hence also range). https://www.selleckchem.com/Bcl-2.html The average estimated SL of 188.5?±?2.1?dB re 1μPa measured between [25-30] Hz for the ABW and 176.8?±?1.8?dB re. 1μPa measured between [22-27] Hz for the MPBW agree with values published for other geographical areas. Units were vocalized at estimated depths of 25.